Machine Learning Algorithms as a Mapping Between Spaces: From SVMs to Manifold Learning

Exploring the beauty of mapping between spaces in SVMs, autoencoders, and manifold learning (isomaps) algorithms

Introduction

In machine learning, understanding how algorithms process, interpret, and classify data relies heavily on the concept of “spaces.” In this context, a space is a mathematical construct where data points are positioned based on their features. Each dimension in the space represents a specific attribute or feature of the data, allowing algorithms to navigate a structured representation.

Feature …

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